1. Field of the Invention
The present invention relates to a method of assessing the health of one or more traction batteries employed in electric and hybrid electric vehicles and more particularly to a method, system, apparatus, and computer product for estimating the state of health of one or more traction batteries arranged in series and/or parallel. Each battery is assumed to be internally composed of one or more cells, also arranged in series and/or parallel.
2. Description of the Related Art
Currently, electric and hybrid electric vehicles employing one or more traction batteries are becoming a popular alternative to conventional vehicles employing an internal combustion engine. Not only are electric and hybrid electric vehicles being phased into traditional automobiles but they are also being implemented in trucks, buses as well as streetcars in so-called fleet operations.
In fleet operations, various vehicle maintenance protocols are typically implemented to assure consistent operation of electric and hybrid electric vehicles. For, example, a vehicle maintenance protocol for a bus fleet would require that each electric and hybrid electric bus be checked by a technician on either a daily or weekly basis to assure that the one or more traction batteries are in proper working order.
The conventional method employed in a typical vehicle maintenance protocol involves one or more of the following methods. One conventional method involves the comparison of battery voltage profiles which requires that a technician remove and access each traction battery to compare the behavior of various traction battery voltages in a pack of traction batteries where data is collected from each traction battery terminal. Outliers from the pack of traction batteries are identified and used to determine if the traction battery is unhealthy. However, outlier analysis has the limitation that it typically requires that several traction batteries be available for comparison. If all the traction batteries in the pack are degrading, outlier analysis would not see any difference between each traction battery.
Another conventional method employs electrochemical impedance spectroscopy where technicians test the electrochemical impedance of a pack of traction batteries after removing each traction battery. Each removed traction battery is then placed on a special test stand and analyzed. However, in fleet operation where a bus for example can have ten or more 600-volt traction batteries integrated deep into the fuselage of the vehicle makes removal of one or more traction batteries very maintenance intensive and costly.
In another conventional method, a technician monitors the open circuit voltage of a single traction battery. If the open circuit voltage is too low, the battery is deemed unusable. However, monitoring the open circuit voltage is a difficult measurement and is difficult to do on an installed battery. Further, in applications where the battery may be recharged, the open circuit voltage changes with state of charge. As it is difficult to know state of charge, it is difficult to quantify the health of the battery.
In yet another conventional method, advanced state estimation techniques are applied to each traction battery using accurate high-frequency data sampling of a single battery. For example, some applications use advanced state estimation techniques to infer battery state of charge or internal impedance. Internal impedance is a common measure for battery health. Typically, battery impedance increases as the battery becomes unhealthy. Internal impedance is not greatly affected by state of charge, hence, it is a good feature for health assessment. Current state estimations techniques are typically applied to a single battery and use highly accurate, high sampling rate data to assess state of charge. Such conventional approaches are used in laboratory applications. In such applications, batteries may only be discharged or use simple charging schemes. For more complex applications, like a stack of traction batteries used in a hybrid electric vehicle with complex charging/discharging cycles, the data is sampled at very slow rates. The voltage and current measurements are typically sampled at much lower accuracy than seen in a laboratory environment.
However, the conventional method employing advanced state estimation suffer from the same drawbacks as the other conventional methods described above, such as being maintenance intensive and costly. Moreover, conventional advanced state estimation also have data bandwidth limitations where the ability to collect high-frequency (kilohertz) data for a large number of traction batteries is not currently available since current applications typically collect data at 1 Hz or slower.
Moreover, none of the above described conventional methods or any other known method in the prior art can detect whether or not a traction battery or a pack of traction batteries is merely suffering what is termed as a “Blue Monday” condition. A Blue Monday condition is a situation where a traction battery sits idle for an extended period of time and shows a degraded status upon conventional assessment techniques. Accordingly, a technician may likely unnecessarily waste time and resources to subject a traction battery to recharging or removal when in fact the battery is merely in a Blue Monday condition.
Having set forth the limitations of the prior art, it is clear that what is required is a method, system, apparatus or computer product which can provide a state estimation technique which can be applied to a single battery without requiring the removal of the battery from the vehicle, can work with low-frequency data sampling and can detect a Blue Monday condition.